Nonetheless, instrument tracking methods are often deployed in difficult scenarios such as bleeding, over or underexposure, smoke, and reflections [1]}. The net effect of these issues increases the missed detection rates in endoscopic surveillance, hampering the adoption of AI-based tools in this context [2]}. Therefore, the development of robust techniques that can be effectively deployed in real endoscopy interventions is very much necessary.
